The task of identifying semantic relations in text is complicated by their heterogeneous nature.

it is often addressed using non-parametric instance-based classifiers like the k nearest neighbors (kNN), which effectively reduce it to measuring the relational similarity between a testing and each of the training examples.


Finally, we lemmatize all extracted verbs.

we end up with quadruples, each of which includes the following: (i) a pattern, i.e., a lemmatized verb, a preposition, or a coordinating conjunction, (ii) a pattern type, i.e., V for verb, P for preposition, or C for coordinating conjunction, (iii) direction, i.e., relative order of the arguments in the pattern (R marks reverse), and (iv) frequency of extraction.


Due to the small size of the individual training datasets and because of the heterogeneity of the examples, we found it hard to train a good model such as SVM or logistic regression.

we opted for a non-parametric classifier: kNN, and more precisely, 1-nearest-neighbor.


Unlike the Avenue formalism used in the work by Font-Llitjós (2007), our MT system is a pure transfer-based one in the sense that a single translation is generated and no language model is used to score a set of possible candidate translations.

we are interested in the unique right answer and assume that an incorrect paradigm cannot be assigned to a new word.


In this stage, the system has accepted c 1 as a possible solution, but it needs to check whether any of the remaining candidates in L is more suitable.

the new strategy is to ask the user about those inflections w / ∈ I(c 1 ) with the highest possible value for G(w , L).


The aim of the experiments is to asses, in a realistic scenario, whether our semi-automatic methodology is valid to find out, for a given word, its most suitable paradigm.

a group of people has been told to add a set of words to a monolingual dictionary using our methodology.


We exploit Japanese as a pivot language, or more precisely terms written in kanjis, to help decomposing the terms of other languages into morphs and associate them with the corresponding kanjis, in a fully automatic way.

kanjis play the role of a semantic representation for morphs.


As for most European languages, French morphology is mostly concerned with prefixation and suffixation.

we are looking for formal analogies of degree at most 3 (ie, 3 factors: prefix ⊕ base ⊕ suffix).


The main problem is that we do not have examples of morphs that are known a priori to be related (like dermato and dermo in the previous example).

we use a simple bootstrapping technique: if two morphs are stored in γ as possible translations of the same kanji sequence, and if these two morphs share a sub-string longer than a certain threshold, then we assume that they both belong to the same morpheme.


It is important to note that this translation process also produce the alignment of the source term into its translation.

it also segments the initial term and label them with the corresponding kanjis.


The reached vertices are expected to be conceptually related and to exhibit translation relations or synonymy, as one can see in these examples.

figure 3 represents a sort of semantic profile of the morpheme ome, in which the kanjis are used as semantic tags, while figure 4 proposes synonyms and quasi-synonyms morphemes of the suffix ome.


(2009) describe a further empirical analysis of the seed construction and its influence on the learning performance and show that size, arity and distinctiveness of the seed examples play various important roles for the learning performance.

the system demonstrated here, called META-DARE, serves as a monitoring and analysis system for conducting various experiments with seed-based minimally supervised machine learning.


With one single example, the system can achieve very good performance.

all four experiments share similar performance in our evaluations.


This constitutes a very important data source for monitoring various applications (business intelligence, product and service benchmarking, technology watch).

numerous research works at the crossroads of NLP and data mining, are focusing on the problem of opinion detection and mining.


(2006) attribute this loss of per-formance to two factors: (i) in the genre of newspaper texts, which the Penn Treebank is based on, questions are not a high frequency syntactic construction, and (ii) if wh-type constructions occur at all in the Penn Treebank, they predominantly involve relative clause constructions or indirect questions, but not unembedded questions.

a parser trained on Penn Treebank data, routinely misanalyses unembedded questions as these other two construction types.


Existing tools for compound splitting for German only offer an analysis of all component parts of a compound at once without any grouping of subconstituents.

existing tools for splitting compounds were adapted to overcome this issue.


The availability of semantic relations, such as part-whole relations, direct or indirect hypernymy, or synonymy, is employed as well.

a modifier or head that is semantically related to the compound determines the correct splitting of compounds into its immediate constituents with high probability.


Further, the performance of such methods is usually better the more diverse the individual systems are (Polikar, 2006).

having three classifiers 7 (compound splitters) available that produce diverse results, the application of a combined method seems reasonable.


Hence, developing automatic discourse type detection, which is of interest to the linguistic community, seems practicable.

the purpose of the present study is to cluster clauses of a text into discourse types, i.e.


A specific aim of this paper was to identify "real-world" effects of quotations.

the first attempt to label quotes was to align quotes with market movements as in (Lavrenko et al., 2000).


positive or negative) as in (Turney, 2002).

in order to collect as strong as possible quotations, a high precision rule classifier selected quotations with three or more adjectives from one category.


the remaining data from both the analysts and CEO data sets).

the clusters, which contained 75% of a single category had their labels propagated and the job titles from the propagated and labelled data were recorded.


This result may be interpreted as to mean that MWEs are fully or partially stored in long-term memory (Pawley and Syder, 1983) and can be recovered as such, thereby relieving short-term memory whose capacity is limited.

the processing of MWEs should be faster in reading and oral production, at least for natives, who are familiar with most of these.


However, as the size of the reference corpus increases, this solution becomes meaningless since high frequencies of occurrences generate high scores for the chi-square.

more and more phenomena appear significant (Kilgarriff, 2005).


As the probability of unknown events is always the same, the resulting variables are not discriminative enough once the order exceeds the bigram.

we only considered this level for our experimentations.


gmeanNGProb, where probabilities of sequences are multiplied as in the classic n-gram model, is also uncorrelated with difficulty.

this lack of association might come from the fact that we multiply probabilities instead of adding them up.


The total cost of o (denoted by C w (o)) is defined as: The cost of a sequence of operations (and in particular of a proof) is naturally defined as the sum of costs of all operations.


While our approach to learning participants is unsupervised once some parameters have been optimized on a small amount of labeled data, we can only obtain a large-scale knowledge base of scripts if we can collect large amounts of scenario descriptions.

the next step must demonstrate that this can be done, without requiring the manual selection of scenarios to ask people about.


Then, we construct an RMRS (Robust Minimal Recursion Semantic) analysis over the dependency parse.

our input processing architecture consists of two grammars -HPSG grammar which produces MRS structures, and Dependency grammar which produces RMRS structures.


Here the following assumptions are taken into account -the shallow processor does not have access to a lexicon.

it does not have access to arity of the relations in EPs.


The dependency relations here reflect the original HPSG analyses in BulTreeBank and are conformant to the dependency relations schema of the CoNLL shared task (2006).

some of them are more specific (such as, obj, indobj, clitic, subj, etc.


Concerning Bulgarian, its rich morphology seems to conflict with the requirements behind the semantic approach.

the adjectives, participles, numerals happen to have morphologically definite forms, while the definiteness marker is not a semantic property of these categories.


When the modifier is definite, then the information is treated only on the syntactic level.

the head is considered semantically definite, and the information is divided between the two levels of analysis.


We do not have a gold standard corpus of dependency trees with manually constructed RMRS.

we cannot determine a real evaluation of the performance of the proposed algorithm.


However, we have a dataset covered by BURGER grammar for which the correct analyses, including MRS, are selected.

we decided to evaluate the algorithm with respect to this dataset.


According to (Mann, 1988), a discourse is coherent if the hearer knows the communicative role of each of its portion; that is, if the hearer knows how the speaker intends each clause to relate to each other.

a summary will exhibit discourse incoherence if the reader cannot identify the communicative intentions of the writer from the clauses or if the clauses do not seem to be interrelated.


An error analysis following the human evaluation has shown that an important source of error in low ranking summaries is question irrelevance.

we plan to test our content organization strategies with a better initial candidate list.


Hence, on a 32-bit desktop computer with about 3 GB of memory available, a corpus of ≈ 52 million characters (≈ 7 million words) can be processed efficiently.

although optimizing the implementation's use of RAM is certainly possible, it is quite clear that possibilities to use the described approach in linguistic practice depend on whether servers with sufficiently large RAM are available, and affordable.


This simple example illustrates two important aspects of continuations: (1) every meaningful subexpression has a continuation; (2) the continuation of an expression is always relative to some larger expression containing it.

when John occurs in the sentence John left yesterday., its continuation is the property .


Table 3 reports all such constraints in the considered data set.

not all relation labels can be compatible with all entity pairs and therefore we decompose the problem in several binary classifications, one for each possible relation label and apply Support Vector Machines (SVMs) to each of these subtasks.


The Roth and Yih data set is not divided in training and test set.

assessment is performed by following the 5-fold cross validation protocol, as in (Giuliano et al., 2007;Roth and Yih, 2007;Kate and Mooney, 2010).


However, text similarity is often used as an umbrella term covering quite different phenomena.

we formalize text similarity and analyze the datasets used for evaluation.


Even though B and E have rather similar content, the content dimension will not become salient because of the dominance of the style dimension.

the better measures for a certain dimension are available, the better this automatic discrimination will work.


When combining only the two content-centric annotators, the correlation is ρ = 0.90, while it is much lower for the other annotator.

we conclude that this dataset encodes the content dimension of text similarity.


Thereby, factors exceeding the similarity of texts are important.

none of the similarity measures significantly outperformed the cosine baseline.


It is important to underline that, for the propagation of the objectivity categories, we only take into account the subjective precision and not the objective one (when we evaluate objectivity and polarity using the Full corpus we obtain a precision of 0.71 and 0.72 respectively).

the propagated precision would be the product of these values (0.51), which is 12% lower than evaluating Obj+Pol together (0.58).


Of course, checking all cases of plagiarism manually is an unfeasible task.

it is necessary to create new systems that are able to automatically detect cases of plagiarism produced.


As shown in Table 1, the intrinsic phase of the system is able to detect the 71% of the plagiarisms included in the suspicious collection and discards the 34% of all text (more than a third of the text).

the next phase of external detection only has to compare the 66% of text of all suspicious collection.


all our examples are related to this domain and especially to the category of place names belonging to the category of proper names but we should mention that our work is also applied to place names regardless of the domain.


For example, in the word ﺑﺎﻟﺮﻳﺎض bi Riadh, the value of the feature ″Tête_EN″ is empty because this word doesn't have a type.


Let's note that the representation of a word-toword translation is not sufficient to generate a well formed NE in the target language.

readjustment rules are necessary and should be associated in translation process.


In fact, it is sufficient to change the inputs (i.e., dictionaries, morphological grammars) of the syntactic grammars for the desired results.

for example, if we want to translate Arabic NE to another language other than French, the recognition module can be reused with some modifications if necessary (related to the specificities of the domain).


We want to study the influence of the training data on the translation results.

we use for our experiments three corpora of different sizes, which have various compilation methods: JRC-Acquis L (a large-size parallel corpus, automatically aligned at sentence level), JRC-Acquis S (a small-size parallel corpus, automatically aligned at sentence level), and RoGER S (a small-size technical manual, manually compiled and aligned at sentence level).


translation, alignment or spelling errors might influence negatively the output quality.


c-structure original word order f-structure dependencies and coreferences i-structure topic-focus articulation a-structure valence  (Bresnan, 2001) for a detailed discussion of lexical and functional categories and the so-called 'coheads').

a d-structure derived from 20would have three nodes for the words dog, chases and cat.


That is, we need only store M probability values in order to find the maximal probability value.

the path P (x), where L(x) =VBN (associating with the verb concern) in Figure 1 is Figure 2: All the semantic arguments of the verb concern can be extracted from this path.


A semantic argument of a verb in the sentence can be represented as a labeled rooted subtree rooted at an internal node and induced by its all descendants.

to find semantic arguments of a verb is to find a set of such subtrees, more precisely, a set of roots.


The TDL language is a language syntactically very similar to the attributes-values structures which are the base of HPSG formalism.

there are several similarities between HPSG and TDL syntax (Krieger and Schäfer, 1994).


The constructed grammar was experimented on LKB platform.

we specified Arabic HPSG with TDL language.


The distribution of the product pages of each category is very skewed in Internet auctions.

the classification model which tackles the ununiformity of the data distribution is necessary.


(2003) also ignored P (c) assuming it is enough small comparing with P (w i |c) but we think P (c) cannot be always ignored and should be calculated especially when the data distributionis very skewed.

we propose the NNB, which is derivable from eq.


Machine translation (MT) technology is becoming more and more pervasive, yet the quality of MT output is still not ideal.

human corrections are used to edit the output for further studies.


If a user's translation skill is good, he should always provide good corrections, while a user with limited translation skill will provide relatively worse corrections.

if we can acquire user quality, we can use it to evaluate the sentence he corrects.


The incompleteness of semantic knowledge bases combined with the difficulty of relating wordings with conceptual representations is an obstacle to the realization of a deep analysis of sentences which would highlight the relations between concepts.

we considered that a lot of sentence characteristics such as the words used, their syntactic category help to detect the presence of a relation.


The subsequent 23 collected by us, also from student subjects, were completely unconstratined.

these narratives exhibit a wide range of linguistic variation both in focus (perspective) and on lexical and construction choice.


big square says "uh uh, don't do that" / pushes little square around / pushes little square around again/ chases little square.

the most recent argument may emerge as a dominant reference pattern for zero anaphora.


Since the subjects were not constrained in their descriptions in any way, the lexical choice and linguistic constructions varied widely.

the same event may be described as "gADI dAe.N se bAe.N or gayI" (car went from right to left), "ek sa.NTro gayI"(one Santro went) etc.


The 'Suffix' method exploited the fact that many nominal components in light verb constructions are derived from verbs.

in this case only constructions that matched our POS-rules and contained nouns that end in certain derivational suffixes were allowed.


The adaptation process involved the development of more fine-tuned and sophisticated methods considering the domain-specific features of the texts and characteristics of the annotations.

in the case of noun compounds, POSrules were extended in order to identify noun compounds with more than two parts (e.g.


Also, in the case of unseen data sets, no significant difference was observed across different frequency bands.

in the following sections, the reported results are averaged over all three frequency bands (as well as over all crossvalidation folds).


Whenever there is a derivation with G such that: there is, by definition, a sequence of transitions with M such that: (where q i ∈ Q, a i ∈ Σ∪{ }, a ∈ Σ * , b i ∈ ∆∪{ } and b ∈ ∆ * for all i) and the state X n is by definition a member of F .

: whenever G generates ab ← , M can recognize a, b .


Negative key phrases refer to phrases that suggest that a "searcher" is not interested in the product.

in the case of an ambiguous query the advertisement should be shown; but if any negative keywords are present in the query, then the advertisement should not be shown.


Consider the keyword "Corolla"; imagine that the word "Corolla" appears in links on pages A, B, C, D. The links on pages A, B and C go to the Toyota Corolla article, while the links on page D go to flower petals.

for Corolla the possible senses are Toyota Corolla and flower petal, with frequencies of 3 and 1 respectively.


We could not consider the existing negative keyword lists from the campaign as a gold standard because they were incomplete, for only a few topics; they also contained intentional misspellings (something that this system does not consider); they contained negative keywords that were related to user intention instead of meaning (such as car rentals instead of purchases); and they contained a few emotional negative keywords indicating that the audience had negative sentiments towards the focus of the ad.

we used a number of ad hoc tests to determine the effectiveness of our system.


Meanwhile the patent and publication analysis is based on the search results of the web search engines that can neither guarantee precision nor recall.

avoiding the negative consequences of these factors and evaluating the quality of the TECH-WATCHTOOL system proper remains an open challenge.


Most of the relations it contains are conceptual, so they are transferable from one language to another.


Let e 1 and e 2 be two English literals, and, for instance, r 1 and r 2 their Romanian equivalents; if e 1 is derived from e 2 with a certain affix, it may be the case, but it is not obligatory so, that r 1 is derived from r 2 with an affix.

in English there are drive -driver and in Romanian şofa "drive" -şofer "driver"; in English there are teach -teacher but in Romanian there are preda "teach" -profesor "teacher"; in Romanian there are bucătar -bucătărie, while in English there are cook -kitchen respectively.


Relating them can (at least partially) solve the famous "tennis problem" of wordnets (Fellbaum 1998: 10).

derivation proved to be the third relation as importance for obtaining good quality lexical chains, after hypernymy and hyponymy (Novischi and Moldovan 2006).


However, after aligning the RoWN to PWN 3.0, one of these identical synsets disappeared.

the notation ".c" becomes void of significance.


In this case, the corresponding field is always set to 1 ("general").

we can check for uniqueness of the combination of a word's textual form and part of speech and analyze the corresponding fields of usage and tables where are used "non-general" words.


The process of new derivatives validation is one that raises many questions and it seems that there are solutions though there are some difficulties in this process.

it is impossible to neglect the aspect of source credibility in the process of word validation.


Based on the proposed typology, these related researches working on Arabic coordination didn't treat all forms of this structure.

the originality of our work is to construct a HPSG grammar covering all the possible forms of coordination and its interaction with the other phenomena such as the ellipse one.


In fact, a coordination particle is non operative.


It is easy to extend and can be used only on UNIX system.


lKB is embedded in Trollet and invisible for the user.


Yet, links of these non-referential noun phrases are useful samples for supervised learning.

it is beneficial to annotate such links as well.


This topology is applied to the high-dimensional data and the SOM learning algorithm tries to create a suitable projection of highdimensional data.

similar feature vectors in the feature space will be close in the projection space.


Compiling and annotating an adequate corpus is not a trivial task; it implies lots of people, resources, time and effort.

we consider that it is important to develop a friendly and useful interface to be able to exploit the created corpus, and transform it into a most accessible resource.


Thus, we consider that it is important to develop a friendly and useful interface to be able to exploit the created corpus, and transform it into a most accessible resource.

in this article, we present the on-line interface that we have developed in order to include the RST Spanish Treebank (da Cunha et al., 2011).


We treat the detection problem as a binary classification task where the documents are said to be plagiarised when their similarity score is above a certain threshold, or not plagiarised if the similarity score is below that threshold.

standard evaluation metrics of precision, recall and F-score can be employed to measure detection performance.


The snippets from the same query were merged into one document and processed by a morphological processor.

for each set of snippets, lemmas (words in a dictionary form) were extracted and their frequencies of occurrence were calculated.


are often used in dictionary definitions.

their presence in snippets can mean that a snippet contains a definition of this phrase or the phrase is used in definition of other term.


The latter indicate relevant semantic entities and constitute a better representation of the semantic document contents (Mitra et al., 1997).

this article will first describe a statis-tical analysis of the corpus titles, for each category (e.g., coverage rate, words number, presence of common nouns, verbs, and so forth).


The work presented in this paper can be categorised as an "unsupervised feature construction" approach, according to (Pan and Yang, 2010).

approaches that try to extend a feature set through the unsupervised extraction of new features share some common ground with our approach.


However, there is a difference in how the values of new features are set: instead of placing the value 1 to the original training vector, if the linked original feature is also 1, the original vector is duplicated, and the value 1 is set in the copy, for the new feature.

each original vector is duplicated as many times as there are augmented fea-tures whose value should be 1 for this vector.


Trying to match such a test vector to a training one that has the augmented, but also their original related features set to 1, may be misclassified in favour of a vector with less magnitude, and possibly with no related features (both original and augmented) set to one.

also this case is unable to fully exploit the augmented features, as it may favour classifying test vectors with augmented features into training vectors without augmented features.


However, the huge amount of information makes necessary to accomplish new methods and strategies to tackle the problem.

sA is becoming one of the main research areas that combines Natural Language Processing (NLP) and Text Mining (TM).


}ل{ The error segment for the extracted stem is }ا{ whereas it is empty for the generated stem.

the system concludes that there is some extra character ا /A/.


Our study indicated that using methods and tools designed for a native speaker spell checking is certain to be inade-quate, especially for highly derivational and inflectional languages such as Arabic.

we adopted methods and tools that meet the SLLs of Arabic needs.


Moreover, those learners want to improve their language skills in order not to fall in the same mistakes very often.

it was appropriate that we developed a diagnosis system, letting the learners find out the correct solution for themselves.


On the contrary, statistical NERC systems require a large amount of manually annotated training data.

factors like the specificity of the domain and the availability of big training data are determinant in order to decide which method to use.


NuERCB is conceived to be used in diverse applications where the response time is a critical factor.

we need NuERCB to have a high processing speed using low memory capacity.


On the other hand, the journalist claims that the president tries to follow his predecessor, whom he strongly criticizes, which reveals a stark inconsistency in the president's policy.

the overall sentiment about him is negative.


It uncovers the poorest performing language -Hungarian, for which we currently have only raw triangulated dictionaries.

this evaluation can serve as a task-based evaluation of the quality of sentiment dictionaries: best performing English and Czech (the most advanced dictionaries) are followed by French, Italian, German and Spanish, in which the lack of all morphological variants results in lower recall, with Hungarian at the end of the list.


Since Wikipedia editors aim at comprehensive coverage of all notable topics and are partial about including alternave lexical representations for them we can assume that if some reversal of a Wikipedia title is a term it should be present among Wikipedia titles.

the titles and reversals collection could be correctly classified into terms and not terms by lookup into Wikipedia titles dictionary.


We rely on an assumption that the editors deliberately decide to include either both or just one of the terms "X Y" and "Y X" into Wikipedia.

we were able to estimate how good the golden standard can be predicted by the model and how precise it is.


The resulting word is known as a lexeme.


These errors can be explained via the characteristics of Arabic: Proper nouns in Arabic are generally standard nouns used as names.

the same word can be used as either noun or proper noun, depending on the context.


Opinions depend on individual's personality, culture and expectations of the society.

opinions are challenging for independent external evaluation and categorization.


However, the authors used a small and imbalanced dataset, i.e., 169 positive and 74 negative sentences.

the data had an inheritably high major class baseline of Accuracy = 70% and F-score = 57%.


Many Arabic NER researchers have employed rule-based techniques (Mesfar, 2007;Shaalan and Raza, 2009) that require experts.

many ML methods, including Supervised Learning (SL) techniques, have been investigated in order to learn NE annotated decisions from training data.


In the next step, the initial patterns are generalised.

all extracted initial patterns should complete the following steps in order to generate the final patterns: 1.


Figure 3 shows the final pattern resulting from the initial pattern, after the constrained processes mentioned above are applied: All final patterns that are generated from the algorithm and their frequencies are first computed, and then gathered to form the pattern set (P).

in the final step, two more patterns were generated from every pattern in P. the algorithm split every final pattern into two parts, where each seed instance is located in the leftmost or rightmost position in the pattern.


So, sportspersons constitute only 19% of all person names that exist in the training corpus, and they occur in a few contexts.

the diversity of contexts in which seeds appear plays an important role in obtaining a trained model with good performance.


This would help the reader use nonlinear reading strategies and facilitate the extraction of information about the selected named entity through the document without reading through the whole text.

the reader can get an immediate flashback to the first time the person was encountered so she can re-read or remember more about this person, or see nearby references to get more recent context, and when done can snap back to the query point and continue reading.


Research on parsing has mostly concentrated on parsing the Penn Treebank (Marcus et al., 1993).

most parsers have probability models that are optimized for the syntactic annotations in this treebank and more generally for the language in the treebank.


(2010) investigate the automatic selection of source domains that are useful for parsing a target domain.

the parser can adapt per document to a new target domain.


LoPar was used in concert with these two parsers for the parser combination experiments due to its human accessible grammar files: rule counts can be directly modified and new rules added to the LoPar grammar.

we can add partially agreeing sentences, in the form of individual rules, from the HCRC data.


Given the larger source domain training set, the best performing condition is the one using sentences with unlabeled agreement, SENTUNLAB.

if the parser has a solid, large grammar from the source domain, it can use the large but noisy addition to its grammar while the smaller source domain grammar requires more high quality additions.


There are no sentences that begin with "and" in the training set, which makes them a challenge for the parsers.

in our best co-training experiment, the Berkeley parser relied heavily on the generic X label.


While this method might be useful and accurate for smaller data with limited entity types and relations, it cannot scale to extract entities and their relationships in web due to the sheer volume and heterogeneity of data.

open domain information extraction systems such as Reverb (Fader et al., 2011), TEXTRUNNER (Yates et al., 2007) and NELL (Carlson et al., 2010) have received added attention in recent times.


For example, for the sentence "Google bought Youtube", the partof-speech structure will be "NNP VBD NNP" and hence it is easy to identify two nouns as concepts and the verb as a relation between these two concepts.

the tuple, bought(Google, Youtube) can be extracted with high confidence.


It should be noted that most of the selected authors posted in many different topics and we collected posts without exclusion of any topics.

author classification had no influence of topic differences.


Each post was used as an independent element.

in each run of 10fold cross-validation for each author 90 posts were used for training and 10 posts functioned as test items.


First, their shortness (maximum 140 characters for tweets) makes them hard to interpret.

ambiguity is a major problem since IE methods cannot easily make use of coreference information.


POS taggers and entity recognisers) do particularly badly on such texts (see Sections 3.5 and 3.6).

we have developed TwitIE -a customisation of ANNIE, specific to social media content, which has been tested most extensively on microblog messages.


Normalisation and emoticons are handled in optional separate modules, since information about them is not always needed.

tokenisation is fast and generic, Figure 3: Configuration options for the TwitIE normaliser while tailored to the needs of named entity recognition.


Another frequently made mistake is tagging proper noun (NN/NNP) -an observation also made by (Ritter et al., 2011).

we use ANNIE's gazetteer lists of personal first-names and cities and, in addition, a list of unambiguous corporation and website names frequently-mentioned in the training data (e.g.


Only 6 CONC patterns are correctly classified for f s ≥ 5 and the system does not detect any patterns of this type when f s ≥ 9.

the recall and precision for this category equals 0.


Combining annotations with the same RadLex ID (RID), the link from one format to the other can be established.

the radiologist can easily navigate from the pathological Leber [liver] (RID58) described in the text to the correlating position in the images.


However, as temporal annotation is expensive, existing datasets are not particularly large, and therefore do not contain as challenging a variety of forms of expression as general, unannotated text.

evaluations using these resources are unlikely to indicate the true variety of forms of temporal expression.


For example, in the scope of NTEs, often the year is not mentioned, as it is documentdependent.

the TIMEN rules for handling NTEs often do not declare any information about years, leaving this to TIMEN's management of reference time (Reichenbach, 1947).


Morphologically rich languages may also require different treatment than English, because of their rich vocabulary.

we report our result on two movie review datasets in two languages -the English IMDB and Czech CSFD datasets.


See Figures 3 and 4 for detailed statistics on datasets.

the global context should help in widely reviewed targets.


In (de Jong et al., 2005) the authors raise the problem of access to historical collections of doc-uments, which may be difficult due to the different historical and modern variants of the text, the less standardized spelling, words ambiguities and other language changes.

the linking of current word forms with their historical equivalents and accurate dating of texts can help reduce the temporal effects in this regard.


For example, the Romanian word campion (meaning champion) has Italian etymology and the etymon campione, which has Latin etymology and the etymon campione(m).

the Romanian word campion and the Italian word campione are cognates, as they share a common Latin ancestor.


In this study we want to assess the influence of syntactic dependencies and subcategorization on semantic classification.

we chose to take our material from a syntactically annotated treebank.


Although semantic information helps to tackle the translation task, the semantic class of a preposition does not perfectly determine the correct translation.

these approaches do not strive to carry out preposition WSD, but to use semantic features in order to more directly map source prepositions to target prepositions (Li et al., 2005), be it rulebased (Agirre et al., 2009) or with machine learning given aligned bilingual data (Gustavii, 2005).


Combining the evidence from Spanish and English performs slightly better than each language separately does.

translations into multiple languages are useful for the case of auf.


The accuracy of a WSD system on the same set of 30 nouns dropped from an average of 85% when using Wikipedia 2007 to 62% when using Wikipedia 2012 (Dandala et al., 2012).

the rapid growth of Wikipedia over the recent years has brought benefits, such as increased word and sense coverage, but it has also led to complications, such as finer sense granularity, resulting in a markedly reduced performance of WSD systems.


As stated by Mitton (Mitton, 2010): 'when people use a rare word, it is very likely to be a correct spelling and not a real-word error'.

a spell checker with a small dictionary would be very likely to raise false alarms over correctly spelt rare words.


However, since Gildea (2001) it is widely acknowledged that parsers have a drop of accuracy when tested against corpora differing from the typology of texts on which they were trained.

we can assume that the performance of DeSR is probably worse when parsing texts belonging to a different textual genre, such as literature or scientific writing.


In addition, observations suggested that more tweet training data would help.

we augmented our approach in three ways: improved handling of unknown and slang words; conversion of unambiguous tags into token prior probabilities; and addition of semi-supervised training data.


Tagging accuracy on tokens not seen in the training data (out-of-vocabulary, or OOV tokens) is lower than that on those previously encountered (see Table 1).

reducing the proportion of unknown words is likely to improve performance.


This modest improvement indicates value in optimising the rare word threshold.

unknown Handling Results we were able to increase part-of-speech tagging performance in three ways: by adapting the idea of normalisation and implementing it with both fixed word-lists (repairing all but 20% of problem tokens), with extra features encoding word shapes to handle OOV terms, and with a  Table 7: Impact of prior labeling and mapping slang to IV terms on T-dev; rare word threshold is 3. more sensitive threshold to inclusion of rare words in the model.


We link the weighted maximum entropy and the optimization of the expected F βmeasure, by viewing them in the framework of a general common multi-criteria optimization problem.

each solution of the expected F β-measure maximization can be realized as a weighted maximum likelihood solution-a well understood and behaved problem.


In many NLP classification applications, the classes are not symmetric and the user has some preference towards a high Precision or Recall of a particular target class.

appropriate tuning of the model is often necessary, depending on the particular tolerance of the application to false positive or false negative results.


However the expected F β is not concave and is rather cumbersome to deal with.

the standard gradient methods do not guarantee optimality of the solution.


Each letter in the infinitive is tagged with the particular alternation pattern the verb exhibits for that infinitive letter, or with 0 if the verb exhibits no alternation in that letter during conjugation.

the verb a tresȃlta (to quiver) is labelled as follows: t r e sȃ l t a 0 0 0 0 a 1 0 t 0 T 1 Here, T 1 encodes the ending pattern received by the class of verbs to which a tresȃlta belongs, as presented in Table 3 along with a few other ending patterns.


The decision to tag signs of syntactic complexity with information about pairs of single conjoins or single bound constituents means that in many cases, subordination boundaries and coordinators lack information on the full set of constituents bounded or linked by them.

signs bounding subordinate constituents are often not matched pairs.


3: For example, in the reference "doctor cured a patient" and candidate "doctor treated a patient", the precision and the recall are respectively 0.75 (= 3 4 ).

the f-measure is 0.75 (= 2×0.75×0.75 0.75+0.75 ), even though there is only one non-matching word.


For example, the weight of each word is 0.05 (= 1 20 ) when the sentence length is 20.

an automatic evaluation metric in which the weight of each mismatched word does not depend heavily on sentence length would be highly desirable.


In "doctor cured a patient" and "doctor treated a patient", the value of LCS is 3 because the matching words are "doctor", "a" and "patient".

the chunks are "doctor" and "a patient".


Through Table 2, the value 0.6774 was the highest value in "Total Avg.".

0.1, 1.2, and 2.0 were determined as the most suitable values of parameters α, β and δ respectively.


Our experimental results indicated that the correlation coefficient of our metric is the highest among some leading metrics.

we confirmed the effectiveness of our metric.


Figure 2 gives an overview of the paper's organization (and at the same time highlights the usefulness of a TOC for the reader).

we may safely skip the enumeration of paper sections and their content that usually concludes the introduction.


In comparison to that, the most-frequent title of the Gutenberg dataset appears twice, only.

we expect the supervised approaches to be most beneficial on the Wikipedia dataset.


Despite the multitude of speakers of Persian (Bateni, 1995), there has not existed any corpus tagged with temporal relations in Persian yet.

as the first step, events and its attributes were tagged in the PTB corpus (Yaghoobzadeh et al., 2012).


Considering that Free Word Orderness (FWO) could have a negative impact on classification, we have aimed to design our Persian Temporal Relation Classifier (PTRC) in a way that prevents side-effects as much as possible.

a simple kernel was applied to a group of lexical and semantic features that were inherently resistant against FWO.


Considering that FWO just changes the children orders of Trans 1 , the path will be FWOresistant.

no extra method is required for tackling the probable side-effects.


Signals are not annotated in the TempEval-2 database.

the Signal-based features are ignored in the TempEval tasks.


Figure 1 shows the structure of a simple inheritancebased lexicon with some abstract high-level structure (CATEGORY, VERB, NOUN), then a layer of lexemes (WALK, TALK, HOUSE, BANK), and below that a layer of word forms (walks, walking, talked, house, houses, banks, as well as many more).

the word form walks inherits information from the lexeme WALK, which inherits from abstract node VERB and then abstract node CATEGORY.


Different statistical models perform differently on different tasks.

in order to find the best practical solution one might need to try several approaches before getting the desired effect.


It is based on a combinatorial construction that allows preserving both: (i) manipulation with single functions and (ii) the usage of the standard multiplication and addition of real numbers.

we get a uniform representation of functions, their first-and higher order derivatives.


Each of the summation terms differs from the original expression for G(λ 1 , λ 2 ) in exactly one multiplier whose partial derivative with respect to λ 1 is computed.

in order to construct a functional automaton for ∂F ∂λ 1 we can take two disjoint copies of the original functional automaton, see Figure 3, and set transitions between them in order to reflect the partial derivatives with respect to λ 1 of the single multipliers.


Formulae very similar to (2) and (3) can be derived.

the main problem is again in the computation of the term Z x (λ).


We can directly construct functional automata for the partial derivatives of F (first order and if needed second order), see Proposition 3.3.

we can use a gradient method to find a local extremum of F .


In this optimization scheme, the bestperforming set of features is selected on the basis of the best F1 "exact" score.

we increase the number of features at each step, and report the corresponding performance.


We also run backward hill climbing technique, and the result is the same with forward hill climbing, because our feature set is fairly small in size.


In particular, a context-dependent aspect appears to be useful, which would allow one to distinguish attributes that stand out in terms of salience (such as color in the furniture domain) from similarly salient attributes -there exist several in the TUNA corpus (such as wearing glasses and beardedness).

the number and complexity of regularities would increase.


To identify difficult categories on challenging tasks, a traditional bag-of-words representation may not be sufficient.

a richer, structural representation is used rather than the traditional bag-of-words.


This is because these algorithms targeted a sentence as their input rather than a document or text consisting of two or more sentences.

even if crucial substructures appear several times in their target texts, these algorithms cannot reflect such frequency.


For example, on sentiment classification, different types of negative expressions may be preferred to a positive expression which ap-pears several times.

it may happen that a positive text using the same positive expression several times with some types of negative expressions is classified as a negative text because consideration of frequency is lacking.


Each node is also ordered among its siblings.

there are a first child, second child, third child, and so on (Abe et al., 2002).


We used the Most Frequent Sense (MFS) as the first baseline for this comparison.

among all the sense annotations for a target word the most fre-  quent was taken and it was assumed that all the occurrences of the target word bore the same sense, referred to as 'GS-MFS.'


The classifiers are then applied to the same data to obtain word clusters relating each word to every other word with respect to either common roots or common patterns.

for the root lexicon we obtain neighbours of words that have the same or similar patterns.


The intuition for adjustment formula comes from the work of (Chung and Gildea, 2009) and (Liang and Klein, 2009), who use a exponential Length Penalty measure to adjust their model for morpheme length.

the pattern is scored according to the score of words containing plausible roots.


Indeed, Petrov and McDonald (2012) mention that for the shared task, "[t]he goal was to build a single system that can robustly parse all domains, rather than to build several domain-specific systems."

parsing results were not obtained by genre.


Given this degree of error in tagging, a parser trained with similar noise in POS tags may outperform one which is trained on gold tags.

we run TnT on the training data, using a 10-fold split of the training set: each tenth of the training corpus is tagged using a POS tagger trained on the other 9 folds.


Interestingly, the best-performing method adds low-perplexity data to training.

selecting data which is more similar to the domain helps the most.


Next, we extract the constituent adjectives and nouns from our test data and populate the semantic space with the words not yet contained in it.

our semantic space contains 8,364 nouns.


It is rarely known beforehand when the next word will be available for processing.

in an incremental pipeline a) computation should continue until the next word occurs if this might contribute to a better result, and b) a new word should be included immediately to avoid delays.


From semantic representations to surface phonologic representations, seven different levels of linguistic representation are supposed for each set of synonymous utterances.

two times six modules containing transformation rules are used to transcribe representations of a level into representations of an adjacent level.


Lefrançois and Gandon, 2013b) proved that in the hierarchy of unit types, if t ∩ 1 ∩ t ∩ 2 then the actantial structure of t ∩ 1 is more specific than that of t ∩ 2 , except for some degenerated cases.

as one goes down the hierarchy of unit types, an ASlot with symbol s is introduced by the radix {γ γ γ(s)} and first defines an optional ASlot for any unit type t ∩ more specific than {γ γ γ(s)}, as long as t ∩ is not more specific than the obligat {γ γ γ 1 (s)} (resp.


Recognizing citation functions is important for understanding the structure of a single scientific document as well as mining citation graphs within a document collection.

this task has attracted researchers from the fields of discourse analysis, sociology of science, and information sciences for decades (Teufel et al., 2006a).


We focus on the manual rule creation approach and discuss to what extent the existing tools can be used for this task.

of our initial research we developed a rule-based toolset for recognition of relations between PNs called WCCL Relation.


A synset is a group of LUs which share all constitutive relations; plWordNet software determines such groupings automatically.

if relation R is noted as linking synsets S 1 and S 2 , it links every pair of LUs s 1 ∈ S 1 and s 2 ∈ S 2 .


Supervised and semi-supervised methods require large treebanks, which may not exist for many languages.

many researchers have focused on unsupervised methods.


Also, semi-supervised methods need a rich corpus that for some natural language (e.g., Persian) does not currently exist.

we have focused our attention on unsupervised methods.


However, in free word order languages such as Persian, words can grammatically appear in different places of sentences.

occurrence frequency of such sub-trees substantially decreases.


These self-citations participate in the same citation relations as conventional citations.

cORROBORATE and cONTRAST relations can have a self-citation arguments, e.g., Figure 1, we and This study in ex.


However, they indeed play an important role in distinguishing some classes of verbs.

embedding CCG categories in the semantic space might be useful to give better representation of the meaning of verbs.


The phrasal SMT does not take the whole context of the sentence into account.

in order to find the correct sentence from the candidate list and obtain a better ranking, we define other features that indicate the affinity of each word in candidate sentences with the whole context.


Furthermore, electronic dictionaries also include all inflected verb forms allowing to process queries expressed also with passive and more generally non-declarative sentences.

we use FSA variables for identifying ontological classes and properties for subject, object and predicate within RDF graphs.


Although a number of language researches has been conducted, especially for languages being so popular and dominant such as English, German or Spanish; little is known about the culturespecificity of Slovak requests.

one of the goals presented in this paper is to provide an insight into culture-specific preferences in Slovak requests.


Though by using software for linguistic data preparation, large amounts of sources can be sorted out and useful information from the individual words, phrases or sentences can be extracted.

the gist of information extraction is the identification of specific information, in our case the expressive and social factors.


The BLEU+1 approximation of BLEU smooths the n-gram counts but not the brevity penalty, thus destroying the balance between the two; it also assigns a non-zero precision to cases with zero matches.

we experiment with a version of BLEU+1 from (Nakov et al., 2012) that smooths the brevity penalty and also uses a "grounding" factor.


For example, recent work has suggested that length is an important factor for parameter optimization in statistical machine translation (Nakov et al., 2012).

we considered how the above strategies would work when selecting not the dataset yielding the highest BLEU, but that for which the source/reference length ratio is closest to 1.


In fact, this is not very surprising: a student would learn more from hard lessons than from easy ones.

the best strategy to prepare for an exam is to learn hard rather than easy lessons.


In order to detect that the context of a phrase ph is limited, we build on the assumption that values lef t div ph(ph) and right div ph(ph) follow a logarithmic curve as occ ph(ph) augments.

the coefficient obtained from dividing the number of different contexts over the number of occurrences should decrease as the number of occurrences increases.


3.3), we chose very loose thresholds in order to evaluate the potential of the process.

a phrase had to occur only twice to be considered (min occ = 2), and, if German, could not have more than 10 tokens (max size l1 = 10).


They were generally evaluated with respect to the differences in performance of a machine translation system.

the phrase translations are not themselves evaluated but their impact on a tool is.


Wikipedia is constantly updated; besides the multiple critiques to the reliability of Wikipedia articles 1 , its peer-review nature repays for it (Giles, 2005).

the automatic recognition of relevant to the patent's topic Wikipedia articles is a first experimental step towards enriching patents indexing with Wikipedia terms.


Our hypothesis is that the description paragraphs would have more diverse matches than the claims, as manual analysis has shown that each claim tends to be more precise and mentions several times the object of the invention.

by splitting the descriptions into paragraphs, we expect to find more Wikipedia article matches to the same patent.


The results were rather discouraging and then we set up a minimum paragraph size of 500 chars.

paragraphs which were shorter than 500 chars were added to the next paragraph.


Since the algorithm produces the same un-weighted tree with more words, we could just eliminate the surplus and keep only the smallest number of words.

from all the feature sets that produced the best result, we consider the smallest feature set to be the most representative for one specific corpus of text.


Moreover, there are much more different possible morphosyntactic tags in the case of agglutinating languages (corresponding to the different possible inflected forms) than in English (several thousand vs. a few dozen).

data sparseness is threefold: • an overwhelming majority of possible word forms of lemmata occurring in the corpus is totally absent, • word forms that do occur in the corpus have much less occurrences, and • there are also much less examples of tag sequences, what is more, many tags may not occur in the corpus at all.


The former estimation will aid in removing sentences which con-tain no or little emotional content and thus don't contribute to the overall polarity of the document and the latter estimation will be used in the final document representation as a surrogate for each sentence.

for each sentence we need to estimate its subjectivity and polarity, that is, build a subjectivity and a polarity detector.


We adopt the same process as above, using the number of sentences in the document m instead of M .

if for example a document has 7 sentences, then their distribution would be {2, 3, 2}.


Due to the number of variations of different document representations that can be explored (e.g., values of parameter M ) and space constraints in this section we will present results with the optimal settings that we've discovered for those parameters 4 .

in this section, all presented results are based on backward balancing where the documents are represented as a sequence of probabilities P r(+1|sentence) and the empty features for small documents are set to 0.


word pairs) are described by probabilities.

results produced by any method, including pattern-based and Distributional Semantics approaches can be used.


A taxonomy structure is mostly a subset of the whole wordnet hyper/hyponynymy structure.

a more general task, for the last phase, is extraction of lexico-semantic relations (sensu stricto), called relation formation in (Yang and Callan, 2009).


Such fine grained distinctions are hard to trace in a corpus, but mostly it is this part of a wordnet that is created first.

we assumed that upper hypernymic levels are already built manually, and what is needed is to expand the wordnet structure towards the lower levels.


Most if not all reliable extraction methods produce KSs for words, not word senses.

we assume that w is a value of support for the given word pair x & y as semantically associated.


The whole activation area expresses a location found by the algorithm for x: however, we also need one particular synset to attach a LU for x.

we look for local maxima of the support value and use these values as the semantic support for the whole attachment areas.


There is no perfect recipe that allows identifying all good and sufficient mappings from all bad and incomplete mappings in a language independent fashion, however, the mapper allows users to decide whether non-mapped segments at the beginning or the end of terms should be allowed or prohibited.

the mapper can be executed in order to allow trimmed mappings, but not to limit non-mappings in-between of mapped segments.


The mapping method is only useful if it is able to identify out-of-vocabulary (OOV) term pairs.

the 1000 randomly selected term pairs from the manual evaluation were looked up in the probabilistic dictionary (for the 733 single-word terms) and in a translation model of an SMT system (for the 267 multi-word terms) that was trained on the same parallel corpus from which the probabilistic dictionary was created.


They used an additional classifier for learning sentence polarity that was trained on external data with user-provided scores.

the PSP values gave a high correlation with document ratings.


Research on sentiment analysis suggests that certain parts of speech, e.g., adjectives, verbs and adverbs, are good sentiment markers (Pang and Lee, 2008).

we represent a document as a vector of unigrams and bigrams and filter out those that do not contain above parts of speech.


We randomly divide our data into 5 folds where one is used for parameter tuning and 4 for testing the algorithms in the cross-validation setup.

in every experiment, 400 examples are used for testing/tuning and the remaining 1600 instances are split into labeled and unlabeled sets.


We then use rules to derive the different pre-processing schemes required for the machine translation experiments.

instead of using MADA (Habash et al., 2010), the well known morphological analyzer for Arabic, we choose another accessible morphological analyzer that is memory-based learning for both word segmentation and Part of Speech tagging (Emad and Kübler, 2010).


The following table (Table 1)   4 Experiments on SMT Our SMT system was trained on 3.5 million words of French and their parallel text in Arabic (equivalent to 108 300 sentences) in addition to 9700 parallel sentences that were extracted from the essentially comparable UN corpus of 2009.

the total number of sentences is 118 000 for the training corpora.


Secondly, existing lexical inventories contain more general vocabulary and have by consequence a low coverage for domain-specific terms.

researchers have started to investigate how semantic resources such as ontologies can be learned from text instead of being created manually.


Second, we observed that semantically related words do not always appear in the same cluster.

correct hypernymhyponym pairs not occurring in the same clus-ters are erroneously eliminated by the filtering module.


The baseline experiments in table 1 show that tagging errors account for about a third of total pars-ing errors.

we investigate ways to improve tagging, and mitigate the effect of tagging errors on parsing performance through nbest-tagging.


Assuming we know all the possibilities, W i , of tagging each of the words w i of the input sentence, the search space of the problem can be defined by the set W 1 ×W 2 ×• • •×W m .

the solution can be found by searching the problem state space.


To encode each of the tags belonging to the tag set, we used a string of 5 bits.

a par-ticle that proposes a tagging for a sentence with n ambiguous words will be represented by n×5 bits.


In well Im keeping my eye on you just so you know, the word eye indicates "anatomical body part" but the message does not refer to personal health.

manual screening of the extracted messages is required in order to remove irrelevant messages.


We use two types of tweets without PHI: a) those preceding the tweets with PHI, b) those following the tweets with PHI.

we state the learning experiments as a three-class classification problem.


This condition was satisfied when discussion had ≥ 10 messages.

for further analysis, we analyzed 74 topics with 10 -20 posts, with an average 12.5 messages per topic.


Each chunk-specific automaton provides an independent BIO tagging, as shown in Table  3.

there are as many new attributes as there are types of chunks in our data.


Unfortunately, training such systems requires large parallel corpora of sentences and their translations, called bitexts, which are not available for most language pairs and textual domains.

building an SMT system to translate directly between two languages is often not possible.


Computing BLEU scores over character sequences does not make much sense, especially for small n-gram sizes (usually, n ≤ 4).

we post-processed the character-level n-best lists in each tuning step to calculate word-level BLEU.


Therefore, we post-processed the character-level n-best lists in each tuning step to calculate word-level BLEU.

we optimized word-level BLEU, while performing character-level translation.


One possibility is to just apply the models tuned for the individual translation tasks, which is suboptimal.

we also introduce a global tuning approach, in which we generate k-best lists for the combined cascaded translation model and we tune corresponding end-to-end weights using MERT (Och, 2003) or PRO (Hopkins and May, 2011).


The final, and probably most important, question with respect to this paper is whether the other languages are still useful for pivot-based translation.

we generated translations of our Macedonian-English test set but this time via Serbian, Slovene and Czech.


We split the corpus into a training set (2369 documents) and a held-out test set (160 documents).

of this preprocessing, we have 2 classes (Location and Negative), 46273 instances, and a target to irrelevant instance counts ratio of 0.17.


http://www.geonames.org/ontology#P.PPLC The Geonames ontology 2 indicates that the candidate is a capital and Gate's ANNIE 3 suggests that the instance is a city.

the conjunction reinforces the recommendation for a location.


The earlier experiments empirically showed that the Similarity Score below 0.2 produced too much noisy data to be useful in SMT experiments.

we proceed with the LEXACC extraction process considering Similarity Score (Sim) higher or equal to 0.2.


Very often, annotators choose for repre-senting the corpus rather than representing the resource.

low frequent senses are not well represented in the training data.


In this time span, 282,503 tokens were double annotated by 4 teams of two annotators, each annotator working 12 hours per week.

80% of the senses received 25 annotated examples or more, and 90% of the lemmas received 25 examples for each sense.


The present paper deals with the detection of anomalies in the content of LMF-standardized dictionaries that covers lexical knowledge at the morphological, syntactic and semantic levels.

we are proposing an approach based on a typological study of the potential anomalies that can occur in editorial dictionaries in general.


With the advent of the computer science, several editorial electronic dictionaries have been constructed to be released from the constraint of their paper versions.

the use of the electronic dictionaries has been expanded to meet the NLP (Natural Language Processing) needs.


On the other hand, a good dictionary must contain accurate knowledge to give the right answers for any use.

it is very important to assess the quality of dictionaries' contents, which is expensive to perform manually and requires high linguistic expertise (Fersoe and Morachina, 2004).


For some other languages such Arabic, the published works still deal with paper versions (Alkhatib, 1967), (Alchidyâq, 1899), and (Hamzaoui, 1986).

we can qualify the evaluation of dictionaries content as very important, notably with an automatic process.


This DC allows us to specify the type of relationship between the lexical entries whether it has a stem or a root.


The sense 1 of word 1 is a synonym with the sense 3 of word 2 and the sense 2 of word 3 is a synonym with the sense 3 of word 2.

transitively speaking, the sense 2 of word 3 and the sense 1 of word 1 are synonyms.


We regard this task as a problem of filtering training sentences with respect to the target domain 1 via different similarity metrics.

we give new insights into when data selection model can best benefit the in-domain translation.


This simple co-occurrence based matching only considers keywords overlap, which may result in weakness in filtering irrelevant data.

it needs a large size of the selected subset (more than 50% of general corpus) to obtain an ideal performance.


In any case, the lexical network that is built is not free of errors which are corrected along their discovery.

a large number of obvious relations are not contained in the lexical network but are indeed necessary for a high quality resource usable in various NLP application and notably semantic analysis.


The use of the mention-pair model implies that an instance consists of a pair of mentions, and, since vectors for machine learning (ML) need to be of a fixed length, each mention is generally represented by its syntactic head, plus informative features that describe the phrases and their context.

there is an additional subtask of CR that needs to be performed before the actual resolution process: mention head detection (MHD).


The languages in the CoNLL-2012 shared task represent a good variation of directionalities: Arabic is a consistently head-initial language; Chinese is a consistently head-final language; and English represents a language with mixed directionality since it places specifiers before the head and heavier constituents, such as prepositional phrases or relative clauses, after the syntactic head.

english is the most difficult case: it requires knowledge of the internal structure of the NP in order to correctly identify the head of a higher-order NP, which is non-trivial to capture in a heuristic or in rules.


This difference is a direct consequence of the various issues specific to English that we introduced in section 3, such as nominal premodification.

heuristich should only be used when it is known that a language is unidirectional.


As discussed in section 4, the MLH approach does not control the number of heads allowed per mention.

a possible improvement of this method can be achieved by an additional restriction on the number of heads allowed per phrase that is bound by the type of NP and the existence of coordinating conjunctions used in the phrase.


Pipeline approaches follow a sequence of processing that reflects the traditional levels of analysis within linguistics: syntax depends on morphology; co-reference resolution depends on morphology and syntax.

dependency arcs are determined by the grammatical features of the wordforms; co-reference chains depend on the grammatical features of wordforms and the configuration of the dependency arcs.


Furthermore, we enable the handling of unknown words by applying a morphological guesser that suggests up to ten possible tags per wordform.

we use the described components to yield a highly accurate and compact set of candidate POS tags.


There are millions of tweets daily, yielding a corpus which is noisy and informal, but which is sometimes informative.

twitter has become one of the most important social informa-tion mutual platforms.


in newswire domain) will transfer to Twitter in order to understand and exploit tweets.

we experimentally evaluate the performance of state-of-the-art POS taggers for MSA on Arabic tweets.


Handling Slang We handle these cases by mapping slangs to their IV equivalents, but slang is an open class and it is difficult to detect all slangs in tweets domain.

we select the most frequent twenty slang words from 17k types in our corpus (10 million tokens) and map them to their IV equivalents.


we will map these tagsets to a unified tagset consisting of main POS tags.


Half of the targeted volunteers were Persian students.

the annotation was performed by people having different levels of education, age groups and sectors corresponding to the Persian society.


In our pipeline, we utilize JAPE rules to identify regular expressions we have formulated as a grammar base for Persian.

together with the gazetteer, this is one of the main contributions presented in this paper.


The Groovy plugin is used to count the number of positive and negative annotations in a given piece of text and determine an overall polarity score.

this can also be considered a third phase in the sentiment analysis, which takes place at the paragraph or entire document level.


However, the information available here was not in a form to enable us to confidently reach conclusive results.

in a second experiment, we again instructed native speakers to rate a large amount of Persian news items and compared their judgment against the ones determined by our pipeline.


The main problem with this corpus is that the reviews are star base, on a scale of 1 (poor) to 5 (excellent) stars.

in order to be able to compare to the results generated by the developed pipeline we were required to map this expression of sentiment as follows: • 1 and 2 stars: Negative • 3 stars: Neutral • 4 and 5 stars: Positive From the above, we generated a corpus of test and evaluation data.


tags, may either be incorrect and/or may not be fully informative of the likelihood of receiving an answer, the number of answers, or question score.

a number of recent studies tried to infer question topics from the natural language used to formulate the questions.


SENTIPOLC dataset is made of a collection of Tweet IDs, since the privacy policy of Twitter does not allow to share the text of Tweets.

we were able to retrieve by the Twitter API the text of only a subset of the Tweets included in the original SENTIPOLC dataset.


In each experiment we added to the baseline (domain dependent features) one of the feature groups described in the previous Section.

we were able to measure the effect that the addition of the features has on the F-measure.


Indeed, several text processing applications based on transducer cascade can benefit from Wikipedia articles.

names of people, which are part of proper nouns, appear frequently in the Arabic Wikipedia.


Applying this formula, we get the value 0.95.

we find that the results for the proposed method are motivating.


It is a core feature of our methodology that the ordinary rule body of a CG parser is used to choose the contextually most acceptable word from a list of correction suggestions.

the best correction candidates are submitted to morphological analysis on par with the original word form, an the result used as input for the tagging stage 1 of the DanGram parser 2 (Bick 2001), whose about 6,000 rules, with their implicit contextual and semantic knowledge, will hopefully sort out the added ambiguity and single out the correct suggestion 3 .


An important difference between our target data and dyslectics texts is lexical variation and word complexity.

we found a much higher percentage of long words and compounds, and there was a higher risk of an "unknown" word in fact being correct rather than an error.


Thus, we found a much higher percentage of long words and compounds, and there was a higher risk of an "unknown" word in fact being correct rather than an error.

we extended the compound analysis module of DanGram as well as its heuristic, endings-based morphological word guesser.


One problem with inflectional error mapping is DanGram's disambiguation, which may well discard correct forms for the sake of erroneous ones if the context also contains erroneous forms.

it may not be possible to re-map a finite verb as infinitive, because the same context that would allow the error-CG to do this, may have led DanGram to discard the verb-reading altogether if the word form as such (or any of its correction suggestions) was, say, a noun or adjective.


On the other hand, many individuals stick to the originally learned spelling over a life time.

danProof adds markers (<frequent>, @green) for "wrong but widely used" forms, making possible an on/off-switch for "strict" spelling errors only.


Confusion without grammatical motivation (@:...) was rarely spotted, but this is probably data-specific for the Greenland setting.

1/3 of the cases were confusion of the subject pronouns 'det' and 'de' which are hard to distinguish contextually, plus cases outside of DanProof's current scope, e.g.


Since the density of errors to be found is very much dependent on genre and text authors, an alternative measure of "experienced performance" is the number of false positives or false negatives per page 14 .

for our essays, DanProof had 0.7 false positives per page with the 'all'-settings, and 0.4 false positives per page with 'spell' settings.


NOMLEX seeks not only to describe the allowed complements for a nominalization, but also to relate the nominal complements to the arguments of the corresponding verb.

with Nomlex we can expand the patterns evoked Methods P R F Sentence-level in (Ji and Grishman, 2011) 67.6 53.5 59.7 MaxEnt classifier with local features in (Li et al., 2013) 74.5 59.1 65.9 Joint beam search with local features in (Li et al., 2013) 73.7 59.3 65.7 Joint beam search with local and global features in (Li et al., 2013) 73.7 62.3 67.5 Cross-entity in (Ji and Grishman, 2011) † 72.9 64.3 68.3 AceJet baseline system 65.4 70.6 67.9 AceJet with dependency regularization 68.2 72.8 70.4 Table 1: Performance comparison (%) with the state-of-the-art systems.


translated from other languages, our goal is to see whether the readability features have enough discriminative power to distinguish original from translated text.

we train a logistic regression classifier 2 for a binary decision problem: original versus translation.


We believe our method can 5 Repeating the classification experiment for each source language (that is, considering translations from each source language Li, except for English, one at a time) shows that the differences in performance are not statistically significant (p < 0.05).

we conclude that readability features cannot discriminate between original texts and translations significantly better for some of the source languages than for the others.


Given the uniformity of Twitter sampling (Kergl et al., 2014), this continuity is unlikely.

we introduce an <EOS> marker after each tweet to break bigrams.


While this shows the effect of cluster quality decreasing when there are both too many and too few clusters, it does not approach the extreme value of c where there is one class for each word type.

we ran another experiment varying cluster size but on a smaller corpus, which allowed examination of performance nearer to c = |V |.


When events are expressed as verbs, tense and aspect are used to convey temporal features of these events.

it is intuitive that tense and aspect will be of value in determining the type of temporal relation that holds between two verb events, and evidence in human-annotated corpora supports this intuition.


Note that HFT model is designed for both predicting ratings and discovering meaningful latent factors.

the goal of our evaluation is to test whether our PAR model can perform similarly to HFT in terms of rating prediction and latent factor discovery, and on top of that, whether our PAR model can well separate product properties and aspects, which HFT cannot do.


However, in all three data sets we use, there is no ground truth and we are not aware of any data set with ground truth labels we can use for our task.


All cited lexicons suffer from a problem concerning their models and contents.

such lexicons need to have a large coverage, to guarantee a high level of quality and to be directly usable in NLP tools.


For these 2,000 affectations, the expert approves that 232 incorrect affectations and 140 missed ones are detected.

for these 2,000 affectations the Precision is estimated to 0.88 and the Recall is equal to 0.92.


Note that in our setting, we do not focus in term/definition pairs, but rather a full-sentence definition.

we do not know a priori which term is the definiendum, and thus we do not perform a generalization step to convert it to a wildcard, which is common practice in the DE literature Reiplinger et al., 2012;Jin et al., 2013;Boella et al., 2014).


head entity, relation, tail entity , which intuitively inspire us to regard the whole repository as a graph, where entities are nodes and relations are edges.

one research community looks forward to predicting unknown relations which may exist between two entities via learning the linking patterns, and another promising research group tries to learn structured embeddings of knowledge bases.


h and t, as a transition, due to the hierarchical structure of knowledge graphs.

we use D r (h, r, t) as follows to denote the plausibility of a triplet (h, r, t) illustrated by Figure 1(a): where the closer h + r is to t, the more likely the triplet (h, r, t) exists.


Given a correct triplet (h, r, t), we aim at pushing all the possible corrupt triplets with wrong relations {r |r ∈ R & r = r} away.

we adopt a margin-based ranking loss function with a block α to separate all the negative triplets in the corrupted base K from all the positives in the correct knowledge base K: in which [ ] + is a hinge loss function, i.e.


Since important phrases can be distributed through different sentences, one needs a sentence boundaryindependent way of extracting both syntactic and discourse features.

we intend to combine/merge parse trees to make sure we cover all the phrase of interest.


To process the second sentence, we need to disambiguate the preposition 'its'.

we conclude from the second sentence that it is a requirements document, not an objectlevel action-plan one.


To solve this problem, the following strategy is adopted: we appeal to the dictionary Delac in Unitex for labelling adjective multiword expressions, adverbial multi-word expressions, verbal multi-word expression and prepositional multi-word expressions ; these expressions are then replaced by the corresponding morphosyntactic label (like <ADV>, <ADJ>, <V> and <PREP>).

the multiword expression une fois acheté becomes <ADV> acheté after the preprocessing.


The morphosyntactic disambiguation of predicates depending on context is conducted at the same time.

the given predicates are labeled by identifying the corresponding verbal phrases with the syntactic patterns such as, avoir+été+ADV+Vpp, se+faire+V, aller+être+ADV+Vpp, se+être+ADV+Vpp, avoir+Vpp, Vpp+Det+N, Vpr+DET+N,.


For example, to decide a lexical unit is a noun or not depends on whether the lexical unit follows an article or not while some articles in French have more than one morphosyntactic interpretations (e.g., un (a) can be an article or a noun).

the entry of un (a) as noun considered less used is eliminated in the preprocessing.


V+NAF+prep+NAF is misrecognized as the syntactic pattern of the predicateéteindre (turn off ) because of the preposition de (of ) which is a constituent of the compound noun lampe de poche (flashlight) rather than a preposition introducing a circumstantial complement.

a probabilistic calculation of syntactic patterns is necessary for choosing the appropriate syntactic pattern for each predicate.


Since LEFT-ARC(N) and RIGHT-ARC(N) result in the removal of a daughter item from the stack or queue, which may be required in subsequent parsing stages, it is vital to ensure that the daughter has collected all and only its daughters.

subtrees can be used to encourage the parser to remove a daughter item only if there is evidence that it has collected all and only its daughters, this corresponds to completeness and cohesion in Lexical Functional Grammar (LFG) (Bresnan and Kaplan, 1982).


Such questions can be answered by studying the sentiment of the opinions people express in social media.

the interest for sentiment analysis, especially in social media, has grown, further boosted by the needs of various applications such as mining opinions from product reviews, detecting inappropriate content, and many others.


For example, searching with tag "London" returns the list of links to all photos annotated with this keyword.

the semantic information, which is saved up in the metadata, enables the development of various searching strategies that rely significantly on automatic text processing, lexical hierarchies and information search techniques.


we receive a set of n+m tags which characterize the image.


The remaining 60% of the experimental dataset are used as a test corpus for classifying images as positive, negative and neutral.

we have a "SMO-baseline" how tags are related to the human judgment of image sentiment.


Even when used, the focus has typically been on having just five categories, not allowing halves.

our contributions in this paper can be summarised as follows: • We create a new dataset for movies in Bulgarian, 1 where each review is associated with an 11-scale star rating: 0, 0.5, 1, ..., 4.5, 5.


For model comparison purposes, we further experimented with aggregated classes.

we ended up with three class inventories of various sizes: • 11-way: includes all labels, both integer and half-star; • 5-way: includes only the full stars; • 3-way: divides the scores into three classes, positive ≥ 3.5 > neutral ≥ 2 > negative.


They are commercially important; if the NEs (named entities) in the area, such as the titles of the anime and the names of the character could be automatically obtained, they would be useful for the product search, identification, or recommendation.

we developed a system to extract Japanese NEs in these areas using Conditional Random Fields (CRF).


NER using pattern matching can extract the NEs that precisely match the patterns, but cannot extract the NEs that do not match the patterns.

it is difficult to use these types of methods in our system because the anime-related NEs such as the titles of an anime often do not match the patterns.


Although the NEs that are irrelevant to anime are not tagged on the anime corpus, because they are outside the scope of our research, the BCCWJ contains many of them.

the anime corpus has many NEs whose POS subcategories are proper name whereas the BCCWJ have many general noun ones.


PerCal searches for periodic patterns at different levels.

it may find multiple patterns in the same date sequence.


It may be relevant to assign to words some information in the form of finite sets of values.

the polarity can be defined by three values: positive, negative and neutral.


• diversity of vocabulary and topics and variability of response: a number of words elicit mixed feelings, even opposing (e.g., the term operating room, theoretically seen as positive, but negative if we are personally concerned), and the feelings of a player can evolve over time and according to circumstances.

the word bachelor or school exam creates a negative feeling among high school students, but significantly positive for graduates.


those that are not strongly neutral, and this while partially avoiding the terms that have already a lot of polarity votes.

a neutral term will be mostly selected through its neighbors.


Some terms are bipolarized because the polarity can vary depending on the diegetic perspective (the player identifies with the character and is involved in the context) or extradiegetic (he adopts an external perspective).


This could be interpreted as 1 all data are freely available at the following url in realtime: url anonymized an analogy to what happens on social networks, where people are invited to click like to show their approval, but where there is no way to indicate that one do not like, disapprove, or even just is indifferent.

it is possible that many people unconsciously behave in a "socially correct" way, i.e.


to propose by deduction and by induction new medical information or even diagnosis.

in medical imaging domain, digitized data is rapidly expanding it is of major interest for radiologists to be able to do an efficient and accurate extraction of imaging and clinical data (radiology reports) which are essential for a rigorous diagnosis and for a better management of patients.


Moreover, the algorithm does not handle negation but in the phrase "no articular fracture", the term "no articular" will be detected because it belongs to the network JDM.

the enlargement is a process intended for adding to the index some terms which are relevant, although they are not in the text.


The use of a wide knowledge base, no limited to the only specialty field would thus improve largely the relevance of the produced index.

the choice not to separate specialized and common vocabulary proves judicious and effective regarding the analysis of radiological reports.


In order to solve this optimization problem, a projected gradient descent has been employed.

this extension to IBM model can be implemented as a modification to the open-source toolkit GIZA++ 1 .


This model usually consists of a feature vector φ and a parameter vector θ.

the joint probability of a logical form z constructed with a parse y, given a sentence x is defined as: In general, traditional CCG lexicon lists lexical items that pair words and phrases with syntactic and semantic content.


Specially, we have made both of original English corpora (i.e., GeoQuery and ATIS) manually translate into the corresponding Chinese pinyin-format string ones by five native quite fluent Chinese speaker, who major in English-Chinese translation during their graduate studying stages.

chinese GeoQuery and ATIS corpora are new.


These grammars are well suited to the semantic parsing because of the close linking with syntactic and semantic information.

they are used to model a wide range of complex linguistic phenomena and are strongly lexicalized, which store all languagespecific grammatical information directly with the words and the CCG lexicon.


In particular, it also contains a factored CCG lexicon from the closed-domain GeoQuery and ATIS.

our approach not only includes two traditional CCG lexicons from the closed-domain GeoQuery and ATIS, and from the open-domain Freebase, but also includes the factored lexicon from the closed-domain GeoQuery and ATIS.


For suffixing languages, a paradigm in a morphological lexicon adds suffixes to a given stem in order to produce surface forms.

a good hint to find out the candidate paradigms from an unknown word is the inflection suffix.


The heuristic-based baseline (Esplà-Gomis et al., 2011) does not depend on the amount of training data and produces an MRR of 0.674.

we can conclude that our machine learning approaches significantly outperform our heuristic baseline.


In the paper we systematically investigate the relations between the distribution of lexical associations in textual entailment data-sets and the system performance.

we define a "resource impact index" for a certain lexical resource with respect to a certain data-set, which indicates the capacity of the resource to discrimi-nate between positive and negative pairs.


From our point of view, then, a multi-word lexical unit is fundamentally an expression built from more than one word, associated with a definite meaning somehow stored in one's mental lexicon and immediately retrieved from memory as a whole.

an MWLU is intuitively perceived by lexicographers as worth including in a dictionary.


Good performance of our procedures on different word combination sets (NA in L3 set, all structural types in L1 and L2 sets), tested by many subjects (2-6, experienced and inexperienced), shows that these procedures are useful.

from lowto-moderate κ, the procedures lift us to the area of acceptable κ.


Unfortunately, we only know fifteen paid trolls (from the publication in Bivol), which is too little to use for training and testing.

we trained on "mentioned" trolls vs. non-trolls, but we then tested on paid trolls vs. non-trolls.


We focused on the top four known paid trolls with the highest number of posts, as they had more than 100 comments, which means that we had enough information about them.

3 for testing we used the four trolls with 100 posts or more, to which we added four non-trolls (i.e., users who have never been called trolls).


Unfortunately, we could not train a classifier to do that directly, as we did not have enough known paid trolls.

we resorted to a simple trick: we considered as trolls those users who were accused of being such by other users.


It follows that it is essential to have a good way of measuring the similarity of sentences, in no way a trivial task.

several measures for sentence similarity have been explored for extractive summarization.


This value when checked in the domain dictionary gives the attribute course name as Mathematics is the name of a course.


This value when checked in the domain dictionary gives the attribute professor name if Smith is a name of a professor.

the attribute professor name is an implicit attribute.


The likeliness of the HAVING clause attribute is again decided based on the local context of the attribute.

gROUP BY clause attribute is not just identified using its local context, but also depending on the presence of HAV-2 http://nlp.stanford.edu/software/lex-parser.shtml INg clause attribute.


As indicated in (Mikolov et al., 2013b), word vectors obtained from deep learning neural net models exhibit linguistic regularities, such as additive compositionality.

σ vn is justified to predict surrounding words of the composition of, say, blow and whistle.


Secondly, we avoid assigning the same value of the measure and the position in the ranking to those candidates that have at least one zero frequency tuple.

candidates with the greater number of zero frequency tuples obtain higher values of the measure, which promotes more fixed word order candidates.


plWordNet editors tried to use the same single constraint set for the description of many MLUs.

a limited set of structural classes of MWEs was defined.


We take it as one of our working hypotheses that language learning is something personal and that text interpretations are greatly influenced by the learner's personality, preferences, experiences, and beliefs which are not something that can be easily set to a particular standard.

tRI systems should be modelled from the learners themselves and that these systems should have the ability to adapt to the learner's learning progression.


In such cases, we assume that these are orthographical or morphological mistakes, rather than collocational ones.


These values indicate that 999 verbs in the corpus have no manually labeled predicate-argument relations.

bRol, which uses EPEC as a training corpus, would only be capable of assigning a    Table 3: Unigram probability is shown for the five most frequent APRED labels in the training data of the CoNLL-2009 Shared Task and in the training data from the EPEC corpus.


Nevertheless, we decided not to implement bRol using separate PC classifiers for two reasons: (1) The size of our training set is too limited for this approach to be effective: we have 11,740 predicate instances and 8,166 correspond to the 80 verbs with multiple senses (69.55%).

the average number of instances available for training each separate classifier is 102.


It is worth mentioning that before running bRol over the test set we deactivated the translation component, since the predicates and their corresponding arguments that would have been labeled as a consequence of the TC are not manually annotated in the test set.

all of these would have been computed as fails although some might be correctly labeled by bRol.


(2010), Gerber and Chai (2012), and also Feizabadi and Padó (2015) for an attempt to enlarge the number of annotation instances by combination of scarce resources.

most state-of-theart iSRL systems cannot be trained in a supervised setting and thus integrate custom, rule-based components to detect NIs.


Clearly, local minima in the plot for level 0 can be associated with the reference frontiers: The number of bursts shared between the utterances at these points are considerably fewer than at any other point.

at this level, the topical segments can be easily identified relying on bursts information.


When going to lower levels, the number of specific bursts decreases and approaches the number of bursts shared.

similar observations as the ones drawn from the counts of bursts (Figure 3) can be made.


This case study leads to several important obser-vations: Frontiers can be identified when there are few bursts across a position and many before/after that position; words that are bursty at one level in the topic hierarchy (i.e., specific at this level) can become general for lower levels in the hierarchy; when going to lower levels in the topic hierarchy, the number of bursts decreases; there are segments with no bursty words.

burst analysis is relevant in the context of hierarchical topic segmentation, but an appropriate way to exploit it has to be proposed; we address this open issue in the following section.


We propose to take advantage of this fact to spot salient topics and sub-topics.

we do not focus anymore on producing a dense hierarchy of segments but instead we aim to derive a hierarchy of topically focused, i.e., salient, fragments which are not necessarily contiguous.


For example, in the case of the nsubj relation, the hyponyms of the dependent node have been replicated in new relations of the same kind, for all hyponyms of that particular word sense encountered in the golden corpus.

the relation u:00118523-v v:00510189-n is derived from an nsubj relation, where 00118523-v stands for a sense of the Bulgarian verb "prodalzha" (continue) and is the head node (the predicate in nsubj), and 00510189-n, corresponding to a particular word sense of "veselba" (revelry), is the dependent node (the subject).


The open class word forms in the BTB are all tagged with their respective word senses, but a big portion of those senses are yet to be mapped to WordNet identifiers.

only a part of the dependency relations from the corpus have been extracted for the purpose of these experiments (because both nodes in a relation must have WordNet IDs).


Another important assumption is that the relations found in the treebank are not the most general ones, which means that there is room for generalization over the participants in these events.

in addition to the extension of the dependency relations outlined above, we did a further enrichment of the knowledge base by taking the hypernym of the node of interest in the syntactic relation and then taking all nodes beneath it in the hypernym hierarchy, and inserting them in 7 Here we interpret the concept of "event" in a wider sense that also includes states.


Returning to the example from above in order to illustrate this strategy, we identify the "revelry" node ("unrestrained merrymaking") as subject of the "continue" node, then we go one level up to its hypernym, which is "merrymaking" ("a boisterous celebration; a merry festivity"), and extend the nsubj relation from there downwards the hierarchy.

the hyponym sense "jinks" ("noisy and mischievous merrymaking") is also inserted in the nsubj relation with the relevant sense of the verb "continue".


This approach yielded poor results, possibly due to the fact that in the PageRank algorithm the contribution of a node weakens the more outgoing edges it has, and the artifical domain nodes have hundreds of outgoing links.

an alternative strategy was adopted of connecting all synsets within a domain to each other.


We gathered posts from discussions marked "inactive" by the forum.

we considered that the discussions have the "last" post.


We estimated "prolificness" of the authors as the ratio of the total number of author"s posts to the total number of posts of the most prolific author in the studied topics (Patil et al, 2013).

prolificness ranges between [0, 1] and the participant with the greatest number of posts has prolificness equal to 1.


It was much easier to predict the characteristics of the message posted by an interlocutor already involved in discussion while a message posted by a person who decided to join this thread was rather unpredictable.

we pooled together messages posted by authors joining discussion for the first time (a.k.a.


1 In order to compare the results of translation experiments among the three corpora, we train and tune all three systems on a similar amount of data.

we focus only a subset of sentence pairs used by Specia (2010), and by Coster and Kauchak (Coster and Kauchak, 2011a).


The corpus model always yields similar paraphrases with adjective or adverb modifiers, making such paraphrases irrelevant for classification.

the corpus model works with only 326 Noun-Noun compounds in Nastase dataset.


Let the training set of n instances the NCs annotated with semantic relations r 1 ....r n respectively, where r i ∈ relation set R. The input x k is represented in terms of modifier and head fea- represent synsets at level i and j in the hypernym hierarchy, combinedly represented as f ij .

the goal is to model the prediction function F : (L×L) R. The SemScat models strives to learn generalized sets of Hypernym synsets, known as Boundary, G. For instance -G 1 = {entity} and G 2 = {physical − entity, abstract − entity, thing} are two boundaries where G 2 is hyponym of G 1 .


We modify the VSM algorithm in case of computing similarity with Relation vectors, in order to allow them to handle the distribution of relations.

the Relation vectors are not converted to unit vector and thus, the VSM computes r 1 cos(θ).


This model achieves an accuracy of 40.72% (baseline 30.55%) and f-score of 38.82% with prepositional vector and (Acc, F) of (34.93%, 35.78%) with verb vector at k = N − 1.

model 6 is selected for the next two experiments.